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PID Plots for Conference

Run 2

Deuteron ID

Plot Description

MC15TuneV1_ProbNNd_0.0_to_1000000.0_LcpKpi.png

ProbNNd shapes for charged tracks from MC.

Deuterons are taken from a 2016 $\Lambda_{b} \rightarrow dp$ sample, and other tracks are from a 2016 $\Lambda_{c} \rightarrow pK\pi$ sample, for all momenta. Decay products in the $\Lambda_{c}$ sample are removed, such that the sample is a close representation of a MinBias sample.

Yields of each track type is normalised to unity.

MC15TuneV1_ProbNNd_0.0_to_1000000.0_LcpKpi_vs_P.png

Profile histogram of ProbNNd in bins of momentum for MC tracks in range 0 < p < 100 GeV.c. MC samples are the same as above. Features in the shapes can be attributed to the RICH thresholds, where separation between the particles changes.

9.3 GeV/c is RICH 1 kaon threshold

15.3 GeV/c is RICH 2 kaon threshold

17.7 GeV/c is RICH 1 proton threshold

29.7 GeV/c is RICH 2 proton threshold

35.4 GeV/c is RICH 1 deuteron threshold

59.3 GeV/c is RICH 2 deuteron threshold

FitToProbNNd_toy_25_29700_35400.png

A template fit to a toy histogram in ProbNNd, with shapes for each component taken from MC (deuterons from 2016 $\Lambda_{b} \rightarrow dp$ sample, all others from 2016 $\Lambda_{c} \rightarrow pK\pi$), and the toy a combination of the MC shapes.

This is for the momentum region 29.7 < p < 35.4 GeV/c, with the relative yields of each of the track types taken from generator-level MC of prompt deuteron production.

Integrated number of entries in the plot approximately matches the number of tracks in the NoBias 2017 data sample for this momentum bin.

Note the variable width bins on the x-axis, which are plotted evenly; the binning was chosen such that the deuteron shape would be flat, so the plot is concentrated in the very high ProbNNd region.

approved_LHCb_Ratios_BothModels_fromHistograms.png

The expected deuteron yield as a ratio of pion yield, in bins of momentum, using two deuteron production models: coalesence and cross-section.

The $p$ and ${K}$ yields are also shown as ratios of $\pi$ yield.

These yields were taken from generator-level MC. Gauss is used to generate the $pp$–collisions, with a function appended to it to form deuterons from $p$ and $n$ pairs in each event.

Approved on 18/09/2018: https://indico.cern.ch/event/757547/contributions/3144861/attachments/1717660/2771699/Plot_approval.pdf
approved_DLLd_PionDeut.png

Plot to demonstrate the separation offered by DLL variables.

Normalised pion and deuteron distributions in DLL$(d - \pi)$ from $\Lambda_{b}^{0} \rightarrow d \bar{p}$ MC sample, with requirement that tracks satisfy hasRICH.

Approved on 18/09/2018: https://indico.cern.ch/event/757547/contributions/3144861/attachments/1717660/2771699/Plot_approval.pdf
approved_AllParticles_Efficiency_DLLd_5_HasRICH.png

In bins of momentum, deuteron-selection efficiencies for deuterons, and mis-ID efficiencies for $p, K $ and $\pi$.

For deuterons, the efficiencies are taken from a MC sample ($\Lambda_{b}^{0} \rightarrow d \bar{p}$),and for $p, K $ and $\pi$ they are taken from PIDCalib. DLL$(d - \pi)$ > 5 && DLL$(d - K)$ > 5 && DLL$(d - p)$ > 5 && hasRICH

Approved on 18/09/2018: https://indico.cern.ch/event/757547/contributions/3144861/attachments/1717660/2771699/Plot_approval.pdf
approved_AllParticles_Efficiency_DLLd_30_HasRICH.png

In bins of momentum, deuteron-selection efficiencies for deuterons, and mis-ID efficiencies for $p, K $ and $\pi$.

For deuterons, the efficiencies are taken from a MC sample ($\Lambda_{b}^{0} \rightarrow d \bar{p}$),and for $p, K $ and $\pi$ they are taken from PIDCalib. DLL$(d - \pi)$ > 30 && DLL$(d - K)$ > 30 && DLL$(d - p)$ > 30 && hasRICH

Approved on 18/09/2018: https://indico.cern.ch/event/757547/contributions/3144861/attachments/1717660/2771699/Plot_approval.pdf

2017

RICH

Plot Description
Plot_2017Validation_KPi_P.png Kaon identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.
Plot_2017Validation_PPi_P.png Proton identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different \DeltaLLPPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.
Plot_2017Validation_PK_P.png Proton identification efficiency and kaon misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different \DeltaLLPK requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

MUON

Plot Description
Plot_2017Validation_MuPi_P.png Muon identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuPi), respectively.
Plot_2017Validation_MuK_P.png Muon identification efficiency and kaon misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuK), respectively.
Plot_2017Validation_MuP_P.png Muon identification efficiency and proton misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuP), respectively.

2016

RICH

Plot Description
MagDown_2016.png Kaon identification efficiency and pion misidentification rate as measured using 2016 MagDown data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.
MagUp_2016.png Kaon identification efficiency and pion misidentification rate as measured using 2016 MagUp data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.
Merged_2016.png Kaon identification efficiency and pion misidentification rate as measured using 2016 data (MagDown + MagUp) as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.
ROC_2016.png Kaon identification efficiency and pion misidentification rate as measured using 2016 data with different \DeltaLLKPi requirements.

MUON

Plot Description
Plot_2016_MuPi_P.png Muon identification efficiency and pion misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuPi), respectively.
Plot_2016_MuK_P.png Muon identification efficiency and kaon misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuK), respectively.
Plot_2016_MuP_P.png Muon identification efficiency and proton misidentification rate as measured using 2017 MagDown data as a function of track momentum. Two different identification requirements have been imposed on the samples, resulting in the open (isMuon) and filled marker distributions (\DeltaLLMuP), respectively.

2015

RICH

Plot Description
S23_KPi_P.png Kaon identification efficiency and pion misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.
S23_PK_P.png Proton identification efficiency and kaon misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLPK requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.
S23_PPi_P.png Proton identification efficiency and pion misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLPPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

MUON

S23_MuMu_MagDown.png Efficiency of the isMuon selection based on the matching of hits in the muon system to track extrapolation as a function of momentum (black) and efficiency of the isMuon selection plus \DeltaLL(\mu-h) >0, where h can be a pion (red), a kaon (blue) and a proton (green).
S23_PiMu_MagDown.png Misidentification probability of pions as a function of momentum after isMuon (black) and isMuon+\DeltaLL(\mu - \pi) >0 (red).
S23_KMu_MagDown.png Misidentification probability of kaons as a function of momentum after isMuon (black) and isMuon+\DeltaLL(\mu - K) >0 (blue).
S23_PMu_MagDown.png Misidentification probability of protons as a function of momentum after isMuon (black) and isMuon+\DeltaLL(\mu - P) >0 (green).

CALO

ePIDVsPiMisID2015.png Electron identification efficiency and pion misidentification rate as measured using 2015 data as a function of track momentum. Two different \DeltaLLePi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively. The plot is made only for electron track for which no bremsstrahlung photons have been recovered.

Run 1

2012

RICH

Plot Description
S20_KPi_P.png Kaon identification efficiency and pion misidentification rate as measured using 2012 data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.
S20_PK_P.png Proton identification efficiency and kaon misidentification rate as measured using 2012 data as a function of track momentum. Two different \DeltaLLPK requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.
S20_PPi_P.png Proton identification efficiency and pion misidentification rate as measured using 2012 data as a function of track momentum. Two different \DeltaLLPPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.

2011

RICH

See the link to the RICH page. In addition the following plots are taken from LHCb Detector performance

Plot Description
Fig38.png Reconstructed Cherenkov angle for \emph{isolated} tracks, as a function of track momentum in the \cfourften radiator. The Cherenkov bands for muons, pions, kaons and protons are clearly visible.
Fig39left.png Kaon identification efficiency and pion misidentification rate as measured using data as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.
Fig39right.png Kaon identification efficiency and pion misidentification rate from simulation as a function of track momentum. Two different \DeltaLLKPi requirements have been imposed on the samples, resulting in the open and filled marker distributions, respectively.
Fig40left.png Pion misidentification fraction versus kaon identification efficiency as measured in 7\,TeV LHCb collisions as a function of track multiplicity. The efficiencies are averaged over all particle momenta.
Fig40right.png Pion misidentification fraction versus kaon identification efficiency as measured in 7\,TeV LHCb collisions as a function of the number of reconstructed primary vertices. The efficiencies are averaged over all particle momenta.

CALO

The following plots are taken from the LHCb Detector performance

Plot Description

Fig44.png Comparison of the Monte Carlo performance of the gamma/pi0 separation tool developed by Yandex (full lines) and the default one (dashed line). The curves display the photon selection efficiency versus the pi0 selection efficiency as determined on a photon sample (B -> K*gamma) and on two pi0 samples (B -> Kpipi0 in blue and B -> J/psi K*[Kpi0] in green). The red dots indicate 95% photon efficency and the corresponding efficiency for pi0. The figure is taken from the proceedings of the ACAT 2017 conference.
Fig32.png Performance of the photon identification. Purity as a function of efficiency for (green) the full photon candidate sample, (blue) converted candidates according to the SPD information and (red) non-converted candidates (left). Photon identification efficiency as a function of \piz rejection efficiency for the $\gamma-\piz$ separation tool for simulation, the red curve, and data, the blue curve (right).
Fig35.png Electron identification efficiency versus misidentification rate.
Fig36.png Electron identification performances for various $\Delta$ log ${\mathcal L}^\text{CALO}(e-h)$ cuts: electron efficiency (left) and misidentification rate (right) as functions of the track momentum.

MUON

See the link to the MUON page

Fig41topleft.png Efficiency of the muon candidate selection based on the matching of hits in the muon system to track extrapolation as a function of momentum for different \pt ranges.
Fig41topright.png Misidentification probability of protons as a function of momentum for different \pt ranges.
Fig41bottomleft.png Misidentification probability of pions as a function of momentum for different \pt ranges.
Fig41bottomright.png Misidentification probability of kaons as a function of momentum for different \pt ranges.

Global

The following plots are taken from the LHCb Detector performance

PlotSorted ascending Description
Fig42left.png Electron identification performance using the $\deltaLLCombepi$ variable, as measured in 8\,TeV collision data, using a tag and probe technique with electrons from the decay $B^{\pm} \to (J/\psi \to e^+e^-) K^{\pm}$. Pion misidentication rate versus electron identification probability when the cut value is varied.
Fig42right.png Electron identification performance using the $\deltaLLCombepi$ variable, as measured in 8\,TeV collision data, using a tag and probe technique with electrons from the decay $B^{\pm} \to (J/\psi \to e^+e^-) K^{\pm}$. Electron identification efficiency and pion misidentification rate as a function of track momentum, for two different cuts on $\deltaLLCombepi$.
Fig43left.png Background misidentification rates versus muon identification efficiency, as measured in the $\Sigma^+\to p\mu^+\mu^-$ decay study. The variables $\deltaLLXpi$ (black) and ProbNN (red), the probability value for each particle hypothesis, are compared for $5-10$\gevc muons and $5-50$\gevc protons, using data sidebands for backgrounds and Monte Carlo simulation for the signal.
Fig43right.png Background misidentification rates versus proton identification efficiency, as measured in the $\Sigma^+\to p\mu^+\mu^-$ decay study. The variables $\deltaLLXpi$ (black) and ProbNN (red), the probability value for each particle hypothesis, are compared for $5-10$\gevc muons and $5-50$\gevc protons, using data sidebands for backgrounds and Monte Carlo simulation for the signal.
-- MariannaFontana - 2016-04-07
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PDFpdf Fig32.pdf r1 manage 36.5 K 2016-04-11 - 11:22 MariannaFontana  
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